Developing and Implementing a Decision Support System-Integrated Framework for Evaluating Solar Park Effects on Water-Related Ecosystem Services

Abstract

In the 21st century, the adoption of solar energy has witnessed significant growth, driven by the increased use of ground-mounted photovoltaic (GPV) systems, recognized as solar farms, which have emerged as major players in this sector. Nevertheless, their extensive land utilization may impact local ecosystem services (ESSs), especially those related to water resources. In the context of the water–energy–food–ecosystem (WEFE) nexus, it becomes vital to investigate how solar park construction will impact water-related ESSs. This paper developed a framework that assesses the effect of constructing a solar park on water-related ecosystem services. We focused on solar farm construction and its interactions with various hydrological cycle components; then, we evaluated the implications for water-related ESSs. This approach encompasses a systematic literature review that identifies the hydrological factors most affected by the construction of solar farms. As a result, thirteen ESSs were selected to be included in an evaluation framework, and a definition of a scoring system of each ESS was defined based on the economic value, a predetermined indicator, or land use and land cover (LULC) properties. The allocation of weighting factors for these scores can be determined based on individual experience and stakeholders. This study presents a DSS-integrated framework to assess the impact of solar park constructions on water-related ecosystem services (ESSs) within the WEFE nexus. The framework was applied to a case study in Darstadt, Bavaria, revealing that, among the water-related ESSs in favor of ground-mounted PV systems (GPVs) compared to traditional agricultural practices, there could be soil erosion and nitrate leaching reduction. The DSS tool enables stakeholders to efficiently evaluate trade-offs between energy production and ecosystem impacts. The findings underscore the potential of integrating renewable energy projects with ecosystem management strategies to promote sustainable land-use practices.

1. Introduction

The global response to climate change is profoundly centered around adopting renewable energy [1]. To reach zero fossil fuel use by 2050, the use of renewable energy may need to increase by six to eight times, assuming that energy demands remain constant at levels in 2020 [2]. Solar power, recognized for its high technical potential among renewable sources, is at the forefront of this shift [3,4,5,6]. By 2100, solar energy is projected to provide 20–29% of global electricity needs [7]. Mirroring this global trend, the European Union aims to achieve at least 32% of total energy consumption from renewable sources by 2030 [8], and photovoltaic systems (PVs) are expected to dominate the renewable energy market by 2050 [9]. A specific instance of this trend is observed in Germany, where in 2020, solar PV systems generated 50 TWh, accounting for over 9% of the country’s electricity consumption, mainly from rooftop and building exterior installations, along with generation in open areas [10,11]. Further, agricultural and forest land types constitute about 82% of the total area in Germany [12], and these landcovers are partly converted for renewable energy purposes [13].

However, expanding solar energy, mainly through solar parks, brings not only ecological considerations [14], but it can potentially alter several components of the hydrological cycle, including precipitation distribution, evapotranspiration (ET), temperature, and radiation at the ground surface [15,16,17,18], and it may also affect water quality through the dilution of impaired groundwater [19]. Ground photovoltaic systems (GPVs) could alter the hydrological cycle [20], groundwater quality, and infiltration rates [17] and impact agricultural land [21]. This, in turn, needs to be examined within the energy–water nexus of an increasing interrelation of water and energy sectors [22]. The growing demand for water in energy production [23], coupled with key water-related challenges such as scarcity and the need for efficiency [24,25], highlight the importance of this nexus. The knowledge on how processes occur in a hydrological cycle is, therefore, crucial when determining the impact of energy production on water [26].

Some studies showed that solar parks, specifically ground photovoltaic systems (GPVs), can significantly impact some of the water-related ecosystem services (ESSs). For instance, Cook and McCuen [27] shows that the construction of panels has a neutral effect on the volumes and timing of runoff; however, the strength of evidence for these effects is considered weak because the conclusion was not based on a real-world experiment; rather, it was based on a model of a solar farm to simulate runoff. Notably, the presence of panels increased peak discharge, imposing the need to implement storm-water management strategies, such as grass cover or buffer strips. Additionally, Choi et al. [28] indicate a strong correlation between solar panel installation and the heterogeneity of soil moisture, leading to increased variability in the moisture content of the soil. This suggests that soil moisture redistribution by panel arrays could be optimized by carefully planning where to plant vegetation in the solar farm to improve plant growth or reduce soil erosion. Further strong evidence indicates that the size of the soil particles increases significantly, while a notable decrease in carbon and nitrogen content of the soil is observed, likely due to topsoil removal during solar array construction [28]. Such changes may compromise soil aggregate stability and chemical quality; therefore, the solar panel constriction practices should be carefully planned, and their long-term ecological impact should be considered, as described by the study of [29]. Temperature regulation is another impacted factor, and the installation of solar panels leads to a sharp decrease in soil temperature and carbon effluxes [29]. However, the results presented by the authors of [30] show a significant decline in the temperature only during summer by up to 5.2 °C, while it increases in winter by up to 1.7 °C, suggesting the impact of solar panels on local microclimates. Plants also experience significant changes due to solar panel construction. Lambert et al. [29] highlighted that above-ground plant biomass and species diversity are significantly decreased.

Barron-Gafford et al. [31] highlight strong evidence that solar panels significantly increase temperatures over photovoltaic (PV) plants, with nighttime readings 3–4 °C warmer than wildlands and decreasing terrestrial albedo. These findings highlight a direct environmental impact of solar infrastructure, particularly in altering microclimatic conditions. Lastly, considering that agricultural activities are a primary anthropogenic source of nitrogen contamination in aquatic ecosystems [32], Mishra et al. [33] address changes in land use from agriculture to solar parks and their strong influence on water-related factors. The study highlighted that such conversion in land use leads to decreased groundwater nitrate contamination, indicating some environmental benefits from this land-use change. It is noted that the impacts of solar parks on the environment are complex and cannot be easily generalized due to varying factors like climate conditions and solar park management. According to Feistel et al. [16], GPV systems can reduce evapotranspiration through shading of the ground and lowering of soil temperature during the growing seasons. Conversely, GPV systems increase soil temperatures during the non-growing seasons as they trap outgoing radiation. However, the overall annual difference in evapotranspiration caused by GPVs is still minor. Moreover, climate changes tend to extend the length of growing seasons, which can further decrease the overall annual evapotranspiration of all types of plants.

The concept of ecosystem services (ESSs) is gaining more attention in scientific discussions and is defined by the Common International Classification of Ecosystem Services (CICES) as “contributions that ecosystems make to human well-being” [34] (p. 3). These services differ from the tangible goods that we receive, such as clean air, water filtration, and pollination, which are essential for our well-being but generally not directly consumed or used. The CICES database was developed by the European Environment Agency for the objectives of environmental accounting, and it was adopted by several researchers and by ecosystem service investigations [35]. The CICES classified ESSs into a five-level hierarchy: (i) section (provisioning, regulation and maintenance, and cultural services), (ii) division (e.g., biomass), (iii) group (e.g., cultivated terrestrial plants for nutrition, materials, or energy), (iv) class (e.g., cultivated terrestrial plants (including fungi, algae) grown for nutritional purposes), and (v) class type (further subdivisions of the class level, specific ecosystem service outputs or beneficiaries).

The complex relationship between developing solar energy technologies and its ecological impacts, especially on ecosystem services, leads to the broader perspective of the water–energy–food–ecosystem nexus (WEFE), underscoring the importance of sustainable resource management and the necessity of cross-sectoral cooperation [35,36]. WEFE expands upon the water–energy–food (WEF) nexus, rooted in integrated water resource management (IWRM), and investigates the interplay between food, energy production, and water provision [37,38]. Addressing and managing such interconnected systems necessitates a holistic approach, considering physical flows, links, and trade-offs among these components [39].

Despite the growing adoption of solar parks, there remains a significant research gap regarding their impact on water-related ecosystem services (ESSs). Existing studies often focus on localized impacts without providing a comprehensive assessment framework that integrates these effects into decision-making processes. Therefore, knowledge about the linkage between solar panels and water-related ESSs is still fragmentarily available and is usually presented on a case-specific basis influenced by individual and local environmental conditions and management practices at a site. To address this gap, this study aims to develop a comprehensive framework to evaluate the impact of solar park constructions on water-related ESSs. The framework aims to assist the translation of scientific information into practice, considering environmental aspects in energy projects, and to develop a flexible DSS tool that evaluates these impacts systematically. This article provides an overview of a newly developed graphical user interface (GUI) that encompasses the framework and its application to a case study evaluating the planned GPV project in Darstadt, Germany, comparing the impact of current landcover management practices with that of a GPV system on a water-related ESS.

2. Study Area

One of the largest ground photovoltaic (GPV) projects in Germany is planned to be built in the Darstadt area, located at approximately 49° N latitude and 10° E longitude in northwest Bavaria, with a total area of 78.2 ha. The construction area is divided into two areas north and south of the Darstadt village, as illustrated in Figure 1. The region falls under Lower Franconia and is witnessing rising water stress due to climate change; this is forecasted to deteriorate further in the coming decades [40]. The water stress, coupled with the intensive agricultural practices taking place in the region, is a leading cause of high nitrate concentration in the local groundwater aquifers [41]. The climate of this area is typically continental, characterized by being dry and warm [42], and the annual precipitation in the area varies from 550 to 600 mm each year [43], which is lower than the average for most parts of Germany. The land use/land cover classification performed by the authors of [44] indicates that the project area is mainly covered with low seasonal vegetation and has strong seasonal variation throughout the year. Table 1 summarizes a collection of critical parameters relevant to the Darstadt area, aimed at understanding the dynamics of water-related ESSs in the area.

According to the “Atlas of Ecosystem Services in Bavaria” (Atlas), the biomass of winter wheat is quantified at 6.57 tons per hectare in Darstadt [45], and the market price for winter wheat is estimated to be 210 euros per ton used [46]. This valuation is exclusive to winter wheat, as no comparable value exists for other crops such as canola. A comparison of the biomass of winter wheat and canola in surrounding areas, as indicated in the Atlas, shows that, while the total biomass of canola per hectare is generally lower, its market price is proportionally higher than that of winter wheat.

Table 1. Key environmental parameters for ESS assessment in the Darstadt area.

Parameters	Value	Source
Rainfall	550–600	[43]
Rainfall runoff erosivity factor (R)	103	[47]
Annual mean temperature	11 °C	[43]
Annual actual evapotranspiration	554 mm	[48]
Grain size distribution	Silt: 74.26%, Clay: 15.0%, Sand/Gravel: 10.74%	This study
Soil erodibility factor (K)	0.40	[47]
Slope length and steepness	2.30	[47]
C (average Germany)	0.126	[47]
Roughness value for the plowed Furrow of 7.5–12.5 cm (P)	0.91	[47]
Soil group	C	[49]
The initial lignin concentration for wheat ranges	22 [%] *	[50]

* The initial lignin concentration for wheat ranges from 18–25%.

Table 1. Key environmental parameters for ESS assessment in the Darstadt area.

Parameters	Value	Source
Rainfall	550–600	[43]
Rainfall runoff erosivity factor (R)	103	[47]
Annual mean temperature	11 °C	[43]
Annual actual evapotranspiration	554 mm	[48]
Grain size distribution	Silt: 74.26%, Clay: 15.0%, Sand/Gravel: 10.74%	This study
Soil erodibility factor (K)	0.40	[47]
Slope length and steepness	2.30	[47]
C (average Germany)	0.126	[47]
Roughness value for the plowed Furrow of 7.5–12.5 cm (P)	0.91	[47]
Soil group	C	[49]
The initial lignin concentration for wheat ranges	22 [%] *	[50]

* The initial lignin concentration for wheat ranges from 18–25%.

3. Materials and Methods

The first step in the methodology involved conducting a systematic literature review to gather insights from prior research on the impacts of solar park construction on water-related ESSs (Section 3.1). Subsequently, the collected data were organized into a database and depicted in a chart, providing an overview of the various interconnected investigated factors (Section 3.2). Afterwards, the related ecosystem services (ESSs) were selected based on objective selection criteria (Section 3.3). Then, a quantitative evaluation framework for these selected ESSs was developed (Section 3.4). Finally, a GUI was developed and tested on a Darstadt case study (Section 3.5).

3.1. Systematic Literature Review

A snowball systematic literature review method was carried out during this research. This method was selected due to its reliability and comprehensive coverage. It uses a proper, clear process of identifying, synthesizing, and evaluating relevant research to address certain questions, ensuring credible, evidence-based answers [51,52]. For this study, three main keywords were identified from the research questions: solar park, water-related, and ecosystem services. Synonyms for these keywords were used to construct a detailed search query in the Web of Science database, combining different aspects with logical operators to ensure a thorough search. The query included terms like solar photovoltaic infrastructure, hydrologic, precipitation, and natural capital, among others, to comprehensively cover the research topic.

The conceptual framework for the systematic literature review focused on the interactions between solar parks, water-related processes, and ecosystem services (Figure 2). Recent studies emphasize that solar farms can modify local microclimates by altering solar radiation interception, which in turn affects soil moisture, evapotranspiration rates, and runoff dynamics [16,53,54]. For instance, shading effects induced by PV panels have been linked to reductions in soil evaporation, potentially benefiting soil moisture retention in arid and semi-arid regions [18,55]. Other research highlights that changes in surface albedo and wind flow patterns due to solar park installations can result in localized temperature variations and altered precipitation infiltration rates, impacting overall hydrological balance [56,57,58]. Additionally, Decision Support Systems (DSSs) have been widely applied to assess trade-offs in renewable energy deployment, particularly within the water–energy–food–ecosystem (WEFE) nexus [59,60,61].

Given these hydrological and microclimatic impacts of solar farms, it is essential to systematically analyze their interactions with water-related processes and ecosystem services. This study employs a structured approach to categorize and examine the relevant literature, ensuring a comprehensive review of these interconnections. The three central themes—solar park, water-related, and ecosystem services—are represented alongside relevant keywords and synonyms. For example, “Solar Park” includes synonym terms like “solar farm” and “PV panel construction”, while “Water-related” involves terms such as “precipitation” and “groundwater”. Similarly, “Ecosystem Services” covers concepts like “natural capital effect” and “ecosystem function”. This framework ensures a comprehensive review of the literature at the intersection of solar energy, hydrology, and ecosystem services. The search encompassed all text from articles in the Web of Science (WoS) database, using a specific search term that yielded 100 results. The WoS database was selected owing to its extensive and reputable collection, especially in the field of natural sciences. This database is noted for its broad inclusion of scholarly content. Its advanced search capabilities, including field-specific options, also played a crucial role [62].

The selection criteria for relevant articles included (i) their focus on ground-mounted photovoltaic (PV) panels, (ii) the description of environmental effects of ground-mounted PV panels, (iii) the examination of effects related to or impacting the hydrologic cycle, and (iv) the emphasis on studies in temperate climates.

3.2. Evidence Database Development

The following data were extracted from the selected literature: (i) Influencing factors such as the management of solar parks and prevailing climatic conditions were examined for their effects on hydrologic processes and environmental conditions. (ii) The components representing the system elements that the construction of solar parks could influence. (iii) The strength of the evidence was classified as either ‘strong’ or ‘weak’, depending on its origin and robustness. Empirical data obtained from field experiments were deemed ‘strong’, whereas model-based projections or indirect observations were categorized as ‘weak’. (v) The effect on the influenced factors, describing both the direction of the change (positive or negative) and its magnitude (small or large). (vi) Any supplementary information that adds depth to the understanding of this study or its outcomes was also included, which might encompass methodological details, limitations of this study, or implications for future research.

3.3. Ecosystem Service Selection

To better assess the impact of solar park constructions on water resources, it is crucial to identify the affected ecosystem services (ESSs) that are related to water. The ESS framework that we propose should facilitate comparison between land-use systems and help stakeholders interpret results. Selecting an ESS for the evaluation framework is hence a key part of this study. CICES database was chosen as the source database. Out of the 90 ESSs described in CICES, the relevant ones for this study were selected based on three criteria. First, we considered if there is a direct impact by hydrological processes defined by [63] on the ESS. For example, the effect of hydrologic processes like evaporation, condensation, and runoff on ESS were considered. However, the evaluation excluded ESSs that were indirectly affected by these processes, such as the aesthetic value of a forest affected by water shortage. Then, we identified the effects of solar farms on hydrological processes and consequently on water-related ESSs based on the literature review. Finally, we focused only on ESSs affected by the transformation of agricultural land or forest into solar farms in temperate climate regions, excluding, therefore, urban areas and other climate regions. In fact, our framework is particularly tailored for assessing ground-mounted photovoltaic parks in temperate regions and should be adapted for its application in other circumstances in terms of hydrological processes and ESSs to be considered. Moreover, we focus on freshwater ESS, and no co-location strategies in solar farms (e.g., agri-PV) were considered.

3.4. Development of Evaluation Framework

The evaluation framework focuses on selected ESSs based on the criteria defined in Section 3.3. The objective is to formulate a straightforward model that informs stakeholders about the implications of converting agricultural or forest lands into solar farms, particularly concerning water-related ESSs. A unique evaluation system was established for each ESS, with details to be discussed in Section 4. The overarching framework employed the multi-criteria decision-making (MCDM) method. The MCDM approach used in this study involves a systematic process for evaluating multiple conflicting criteria, particularly ecosystem services (ESSs). The method includes the selection of relevant ESSs based on their hydrological impacts, assigning weights directly through the DSS tool by users and applying a weighted sum model to calculate an overall performance score for each management alternative. This approach ensures that the evaluation reflects diverse stakeholder preferences and local environmental conditions. MCDM operates on selecting criteria (in this case, ESSs), choosing alternatives (to be performed by users), selecting aggregation methods, and, finally, selecting alternatives based on weighted or outranking factors.

Specific evaluation methods, such as economic valuation [64], indicator-based or LULC assessments, were defined for each selected ESS. These methods were chosen to suit the nature of each ESS. For instance, economical methods are used for provisioning services like cultivated plants, while indicators like the Universal Soil Loss Equation (USLE) [65], runoff curve number (CN), and precipitation [66] are employed for services like control of erosion rates and hydrological cycle regulation, and LULC is used to assess the regulation of the chemical condition of freshwaters by living processes [67]. The aggregation method in the framework integrates these varied evaluation methods through a scoring system [68]. Each ESS was assigned a score, which was then weighted based on its relevance to the specific context of a solar farm. These weights (βi) were determined through stakeholder and expert interviews, ensuring that the framework reflects the priorities and conditions of the assessed area. The overall value of an alternative (such as converting land to a solar farm) was calculated by summing the weighted scores of all relevant ESSs. This method allows for a comprehensive evaluation of the impact on ecosystem services, considering the multifaceted nature of environmental effects and the varying importance of different services in different contexts.

The scoring systems that use indicators or land-use metrics assign scores ranging from 1 to 100. In contrast, when scoring is based on economic value, normalization is required as per the following formula (Equation (1)):

$$x_i=100\times {\displaystyle \frac{({x_i}^{\prime }-{{x_i}^{\prime }}_{min}) }{({{x_i}^{\prime }}_{max}-{{x_i}^{\prime }}_{min})}}$$

(1)

where $x_i$ represents the final normalized score, x_i′ is the market price, x_i′_min is defined as zero, and x_i′_max is the highest economic value among the alternatives. For example, for the comparison between maize production and solar farm construction, x_i′_max for ESS1 would be for the highest value between the economic value of maize for nutrition and the economic value of solar energy. The weighted normalized scores of different ESSs were used to derive a composite final value as shown in Equation (2).

$$Final value=\Sigma {\beta }_i\times x_i$$

(2)

where ${\beta }_i$ is the weight provided by the stakeholder, and the sum of the weights must equal one. In the current framework, data input variability was considered by selecting a range of plausible input values derived from documented case studies and peer-reviewed sources. Additionally, the framework was designed to allow users to directly assign weights to the selected ecosystem services (ESSs) through the DSS tool, promoting a flexible and user-driven evaluation process. This approach enables stakeholders, such as environmental experts, local authorities, and land managers, to reflect their priorities and preferences in the assessment. By integrating both flexible input data and customizable weighting, the framework enhances the adaptability and relevance of the DSS, making it suitable for diverse contexts without the need for pre-defined weighting schemes.

The following Section 3.5 details the development of the DSS tool, focusing on the implementation of these framework components into a user-friendly and accessible software environment.

3.5. Decision Support Tool Development

Building upon the evaluation framework outlined in Section 4.4, the Decision Support System (DSS) tool was developed to facilitate the assessment process in a practical and user-friendly manner. The DSS integrates compensatory MCDM-based scoring systems and weighting mechanisms, presenting them through an intuitive GUI that allows stakeholders to input data, adjust weights, and visualize results effectively. Designed to simplify the comparison of ecosystem services (ESSs) in energy projects, the tool enables land managers to assess the impact of installing solar farms relative to existing ESSs without requiring a deep understanding of mathematics or programming. This section elaborates on the technical and design aspects of the DSS tool, emphasizing how the theoretical framework was transformed into a functional decision-making application.

Nielsen Norman Group [69] introduced ten usability heuristics to provide a user-friendly graphical user interface (GUI), and all heuristics were considered during the development process of the DSS tool. The GUI was constructed utilizing the PyQt5 library. This GUI is composed of 15 distinct windows, including (i) the main window (this window appears first upon launching the software, providing a space for users to input project information); (ii) the DSS window (which enables users to assign weights to each ESS and provide the appropriate information related to it), and (iii) 13 information windows (each of them offers detailed insights about an individual ESS and the rationale behind its calculation process).

4. Results and Discussion

In this section, we present a comprehensive review of water-related ecosystem services (ESSs) associated with solar parks, derived from an extensive literature review and including the relevant equations used to quantify these services. Our aim is to provide a detailed overview of the current understanding and methodologies employed in evaluating water-related ESSs in the context of solar parks.

4.1. Literature Review and Database Development

Six primary articles were initially selected [15,18,27,28,29,30]. Further examining their references led to the identification of two additional papers [31,33] as the secondary literature. Each article undergoes a thorough analysis based on various criteria mentioned in the Methodology Section. Such a database reflects the fact that the assessment of the impact of solar parks on water-related ESSs is a novel topic. It also allows us to highlight the complex interaction between the construction of solar panels and their effects on various environmental factors.

As highlighted in the Introduction Section, there is an interconnectedness among the various impacted factors. This is illustrated in Figure 3, which provides a visual synthesis of the observed relationships. The figure outlines the multifaceted impact of solar farm construction on water-related ecological factors. It serves as a preliminary framework, mapping out the causal relationships among various elements influenced by the presence of solar panels. For instance, the figure shows how panels affect wind patterns, shading, surface albedo, and other factors that influence soil and atmospheric conditions such as soil compaction, erosion, and temperature variations. These changes cascade through the ecosystem, altering the soil microbial community, evapotranspiration rates, and plant biomass, ultimately impacting broader environmental metrics such as organic matter decomposition, greenhouse gas emissions, and primary productivity. It is important to note that this diagram represents an initial step in establishing a comprehensive evaluation framework and does not encompass the entire complexity of ecological interconnections, including processes at various spatial and temporal scales. However, it provides a foundational understanding of the water-related dynamics altered by solar farm construction, which is crucial for developing sustainable energy solutions.

4.2. Ecosystem Service Selection

Table 2. Criteria fulfillment of selected ESS.

#	ESS	Criteria 1 (Direct Hydrological Impact on ESS Based on CICES)	Criteria 2 (Impact of Solar Farms as Identified in the Literature Review)
1	Cultivated terrestrial plants (including fungi, algae) grown for nutritional purposes	Precipitation Infiltration Percolation Transpiration Runoff	Heterogeneity of soil moisture [28], soil compaction [27], soil chemical quality [29], daily temperature variation [18,30]
2	Fibers and other materials from cultivated plants, fungi, algae, and bacteria for direct use or processing (excluding genetic materials)
3	Cultivated plants (including fungi, algae) grown as a source of energy
4	Wild plants (terrestrial and aquatic, including fungi, algae) used for nutrition
5	Fibers and other materials from wild plants for direct use or processing (excluding genetic materials)
6	Wild plants (terrestrial and aquatic, including fungi, algae) used as a source of energy
7	Control of erosion rates	Precipitation Infiltration Runoff	Soil erosion [27], soil compaction [27], vegetation community composition [30]
8	Buffering and attenuation of mass movement	Infiltration Precipitation	Soil erosion [27], precipitation (local distribution) [30]
9	Hydrological cycle and water flow regulation (including flood control, and coastal protection)	Evaporation Condensation Precipitation Interception Infiltration Percolation Transpiration Runoff Storage	Precipitation (local distribution) [30], above plant biomass [30]
10	Decomposition and fixing processes and their effect on soil quality	Precipitation Infiltration	Decomposition rate [30], precipitation (local distribution) [29], soil microbial community [30]
11	Regulation of the chemical condition of freshwaters by living processes	Runoff Storage	Groundwater nitrate contamination [33]
12	Ground (and subsurface) water for drinking	Runoff Storage	Groundwater quality [33]
13	Ground water (and subsurface) used as a material (non-drinking purposes)

provides a detailed summary of how each ecosystem service (ESS) aligns with the first two selection criteria: (i) the direct impact of hydrological processes and (ii) its relevance to the effects of solar farms. This comprehensive analysis is a crucial part of this research, offering insights into the intersection of ecological benefits and the environmental impacts of renewable energy infrastructures. It categorizes various ESSs, such as the cultivation of terrestrial plants and the control of erosion rates, assessing them against these criteria. For instance, the table evaluates how the hydrological cycle influences each ESS, considering factors like precipitation, infiltration, percolation, transpiration, and runoff. Precipitation provides essential water input, influencing soil moisture and availability for plant growth. Infiltration ensures that water penetrates the soil, supporting groundwater recharge and maintaining soil health, while percolation allows water to move deeper, replenishing aquifers for long-term storage. Transpiration maintains local humidity and supports vegetation, which is crucial for biomass production, and runoff regulates erosion control and sediment transport, safeguarding soil stability and water quality.

Simultaneously, it assesses the compatibility or impact of these services with respect to solar farm development, including aspects like land use, albedo changes, and habitat alterations. This synthesis is pivotal for understanding the multifaceted interactions between ecosystem services and renewable energy development, particularly in the context of solar farms. It offers a foundational perspective for this research, setting the stage to develop a comprehensive framework in the subsequent section.

Furthermore, the exploration of ecosystem services (ESSs) has clarified the impacts of various influencing factors and identified their respective roles, as outlined in Appendix A,

Table A1. Database for Literature, Considering Impacting and Impacted Factor on Water-Related ESS.

Source #	Influencing Factor	a/b *	Influenced Factor	Strength of Evidence	Effect on the Influenced Factor	Additional Info
[27]	panels	a	runoff volumes	weak	neutral
[27]	panels	a	runoff peaks	weak	neutral
[27]	panels	a	time of runoff peak	weak	neutral
[27]	ground cover: gravel or bare ground	a	peak discharge	weak	significantly increased	storm-water management needed, grass cover or buffer strip after the most downgradient row of panels recommended
[27]	panels	a	erosion	weak	increased	erosion at the base of the panels due to the kinetic energy of the flow that drains from the panels greater than that of precipitation
[27]	panels	a	heterogeneity of soil moisture	strong	increased	heterogeneity in the spatial distribution of soil moisture
[28]	panels	a	particle size	strong	significantly increased	the redistribution of soil moisture by panel arrays could potentially be used in concert with planting strategies to maximize plant growth or minimize soil erosion
[28]	panels	a	carbon, nitrogen content	strong	significantly decreased	likely caused by the removal of topsoil during the array’s construction
[29]	panels	a	soil aggregate stability	strong	decreased	leads to degradation of soil physical quality
[29]	panels	a	soil chemical quality	strong	decreased
[29]	panels	a	soil temperature	strong	decreased	by 10%
[29]	panels	a	soil carbon effluxes	strong	decreased	by 50%
[29]	panels	a	early successional plant communities	strong	neutral
[30]	panels	a	temperature (summer)	strong	significantly decreased	up to 5.2 °C
[30]	panels	a	temperature (winter)	strong	increased	up to 1.7 °C
[30]	panels	a	daily temperature variation	strong	significantly decreased
[30]	panels	a	above ground plant biomass	strong	significantly decreased	up to 4 times lower, explained by microclimate and vegetation management
[30]	panels	a	species diversity	strong	significantly decreased	explained by microclimate and vegetation management
[30]	panels	a	photosynthesis	strong	decreased	explained by microclimate, soil, and vegetation metrics
[30]	panels	a	net ecosystem exchange	strong	decreased	explained by microclimate, soil, and vegetation metrics
[30]	panels	a	surface albedo	weak	decreased
[30]	temperature (air and soil)	b	GHG emissions	weak	positive correlation	generally, productivity and decomposition to CO2 will increase with soil moisture as there is an upper threshold above which rates decrease, reflecting the response of different plant species to varying soil moistures and the inhibition of decomposition under anaerobic conditions
[30]	soil moisture	b	productivity	weak	positive correlation
[30]	soil moisture	b	decomposition to CO2	weak	positive correlation
[30]	precipitation	b	soil moisture	strong	positive correlation	changes in soil moisture directly affected by solar farms are governed by perturbations to both precipitation and evapotranspiration rates
[30]	evapotranspiration rate	b	soil moisture	strong	negative correlation
[30]	panels	b	wind patterns	weak	direction unclear	the areas under the footprint of the panels will receive less, and areas at the edges of the panel will receive more through drainage from the panels
[30]	panels	b	heterogeneity of local distribution of precipitation	weak	significantly increased	the impact on evapotranspiration is less clear, and we purport that it will depend on the park design, with potential for increased or decreased rates contingent on whether the surface roughness, and therefore turbulent exchange, is increased or decreased, respectively
[30]	panels	b	evapotranspiration	weak	direction unclear	hypothesis: solar parks will have substantial effects on the amount of PAR received through the interception of a large proportion of the incoming direct and diffuse radiation
[30]	panels	b	photosynthetically active radiation (PAR)	weak	decreased
[30]	photosynthesis	b	productivity	weak	decreased
[30]	productivity	b	C sequestration	weak	decreased	soil C sequestration may increase or decrease, with decreases more likely in regions where low radiation conditions prevail and increases are more likely in areas subjected to higher radiation levels
[30]	panels	b	vegetation community composition	weak	direction unclear
[30]	vegetation community composition	b	decomposition rate	weak	direction unclear	solar farm-induced microclimates on plant productivity may indirectly affect decomposition rates through changes in the quantity and quality of C entering the soil as litter, and
[30]	litter input	b	soil carbon cycling	weak	direction unclear	additional litter inputs may increase soil C but can also stimulate increases in soil organic C mineralization and respiration if soil microbes are C-limited
[30]	vegetation community composition	b	soil microbial community	weak	direction unclear
[31]	panels	a	temperatures over the PV plant	strong	increased	temperatures over a PV plant were regularly 3–4 °C warmer than wildlands at night, which is in direct contrast to other studies based on models that suggested that PV systems should lower the terrestrial albedo from ~20% in natural deserts to ~5% over PV panels
[31]	panels	a	terrestrial albedo	strong	decreased
[33]	land-use change (from agriculture)	a	groundwater quality	strong	increased	agricultural activities are considered the primary anthropogenic source of nitrogen contamination in aquatic ecosystems
[33]	land-use change (from agriculture)	a	water supply	strong	increased
[33]	land-use change (from agriculture)	a	groundwater nitrate contamination	strong	decreased
[18]	panels	a	atmospheric temperature	strong	daytime warming and nighttime cooling	larger variations during high-temperature seasons, and warming occurs for a longer duration during spring and summer compared to autumn and winter
[18]	panels	a	relative humidity	strong	decreased in shaded area	non-shaded area shows a slight humidifying effect (1% to 3%)
[18]	panels	a	soil water content	strong	increased in shaded area
[18]	panels	a	soil temperature	strong	decreased in shaded area
[18]	panels	a	wind speed	strong	decreased in shaded area, increased in non-shaded area

* Factors that cause the effect are classified as (a) solar park management and/or infrastructure (e.g., PV array width, solar panel angle) and (b) climatic conditions (e.g., temperature, precipitation, soil type).

. The appendix delineates the selected ESSs, detailing their utilization, a simplified descriptor aligned with the Common International Classification of Ecosystem Services (CICES) framework, and general information pertinent to each service. The ESSs range from cultivated plants for nutritional purposes to regulating freshwater’s chemical condition by living processes. This comprehensive tabulation provides an overarching view of the interaction between these services and their ecological contributions, which are vital for understanding the holistic impact of ecosystem services on environmental sustainability and human welfare.

4.3. Framework Evaluation

The evaluation methods applied to the selected ecosystem services (ESSs) are outlined, categorizing each ESS by the specific method used: economic and biophysical indicator. Provisioning (biotic) ESSs numbered 1 through 6 are assessed using economic methods, focusing on these services’ financial aspects and impacts. The ESSs 7–13 were used as biophysical indicator methods. For each selected ecosystem service (ESS), a scoring system was established to evaluate its performance. To measure the overall contribution of ESSs, it is essential to assign weights (βi) to each of them. Such weights are generally estimated through stakeholder interviews [70].

4.3.1. Economic Method

According to the CICES database, ESS 1–6 are categorized under provisioning ecosystem services (ESSs). Carson and Bergstrom [71] suggested that the market price method is used for the evaluation of these services, where it employs the prices of commodities and services traded in commercial markets to determine the value of an ESS. The calculation of scores for ESS 1–6 is based on the normalization formula outlined in Equation (1) and on Equation (3).

$$x_{1-6}=100 \times {\displaystyle \frac{economic value of ESS}{maximum economic value of alternatives}}$$

(3)

4.3.2. Biophysical Indicator Method

Control of Erosion Rates (ESS₇)

The current evaluation framework assesses the ecosystem service (ESS) related to the control of erosion rates using the Universal Soil Loss Equation (USLE) [65]. It calculates soil loss with the following equation:

$$A=R\times K\times L\times S\times C\times P$$

(4)

In this equation, A represents the predicted soil loss, R is the rainfall-runoff erosivity factor, K is the soil erodibility factor, L is the slope length factor, S is the slope steepness factor, C is a cover management factor, and P is a supporting practice factor, as outlined by [72]. Kayet et al. [73] categorized soil erosion into five classes: slight (0–5 t/ha⁻¹ yr⁻¹), moderate (5–10 t/ha⁻¹ yr⁻¹), high (10–20 t/ha⁻¹ yr⁻¹), very high (20–40 t/ha⁻¹ yr⁻¹), and severe (over 40 t/ha⁻¹ yr⁻¹). The evaluation framework adopts these classes to define the scoring system for the ESS control of erosion rates, as shown in

Table 3. Scoring system for erosion ESS.

Soil Loss (Calculated with USLE) (t/ha−1 yr−1)	Score (×7)
0–5	100
5–10	75
10–20	50
20–40	25
>40	0

.

Buffering and Attenuation of Mass Movement (ESS₈)

The landslide susceptibility assessment often considers factors such as lithology weathering, slope, aspect, land cover, proximity to streams, and drainage density [74]. In the context of solar farm construction, the primary factor impacted is land cover, making it the foundational element for evaluating this ecosystem service (ESS). Riis et al. [75] assessed the ESSs provided by riparian vegetation. For the ESS associated with the buffering and attenuation of mass movement, they categorized the benefits offered by herbs or grass as low, while those provided by forests were categorized as high. Garcia-Chevesich et al. [76] indicated that agricultural irrigation could significantly trigger landslides by saturating subsurface materials.

The influence of solar farms on landslide likelihood is not well documented. Therefore, the weight assigned to this ESS should be carefully considered due to the high level of uncertainty. While deforestation, for solar farm construction, is known to heighten landslide risks [77,78], the comparative impact of crops and solar farms remains unexplored. This evaluation framework assumes that agricultural cropland and solar farms pose similar landslide risks, based on a trade-off analysis. First, the non-irrigation of land used for solar farms reduces landslide risk, in contrast to the landslide-triggering irrigation practices in agricultural lands. Second, the construction of solar farms typically involves removing vegetation under photovoltaic panels, which increases landslide risks, whereas agricultural land usually maintains its vegetation cover. Lastly, local regulations for solar farm construction often require landslide risk assessments, which could help mitigate the landslide risks associated with solar farms. Given that forests significantly reduce landslide risks, while agricultural cropland is viewed as a risk enhancer, the scoring system for this ESS allocates 100 points for forests but 0 points to both agriculture and solar parks.

Hydrological Cycle and Water Flow Regulation (ESS₉)

The SCS Curve Number Method is employed to evaluate this ecosystem service; the method is developed by the Soil Conservation Service (now part of the USDA Natural Resources Conservation Service) and is a widely used technique to estimate the amount of runoff from rainfall in a watershed where direct runoff measurements are unavailable [66,79]. This is achieved by using an index that reflects the runoff response. Natural features such as forests, wetlands, and floodplains are recognized for their role in regulating runoff and increasing infiltration. Key factors that influence the curve number include land use, land cover, the hydrological soil group, moisture conditions, and the slope of the land.

The publication “Urban hydrology for small watersheds” (TR-55) [80] provides a detailed estimation of CN values based on various factors like hydrologic soil group (HSG), cover type, treatment, hydrologic condition, antecedent runoff condition (ARC), and the proportion of impervious area in the catchment. For the purposes of this evaluation, the following Equations (5)–(8) are employed:

$$S_{max} ={\displaystyle \frac{CN}{25400}}-254$$

(5)

$$I_a=0.2\times S_{max}$$

(6)

$$\mathrm{Q}={\displaystyle \frac{{(\mathrm{P}-I_a)}^2}{\mathrm{P}-I_a+S_{max}}}$$

(7)

$$x_9=\left(1-{\displaystyle \frac{Q}{\mathrm{P}}}\right)\times 100$$

(8)

Decomposition and Fixing Processes and Their Effect on Soil Quality (ESS₁₀)

Meentemeyer [81] devised a model to calculate the annual decay percentage, incorporating climatic and chemical/physical factors. The model uses annual actual evapotranspiration (AE) to reflect climatic controls and the concentration of lignin (L) in organic matter for chemical and physical controls. The formula is represented as follows:

$$A\mathrm{W}\mathrm{e}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{t}\text{\_}\mathrm{l}\mathrm{o}\mathrm{s}\mathrm{s} = (-3.44618 + 0.10015\times \mathrm{A}\mathrm{E}) - (0.01341 + 0.00147\times \mathrm{A}\mathrm{E})\times \left(\mathrm{L}\right)$$

(9)

where annual AE is in mm, and L is the initial lignin concentration in percent.

Subsequent studies have utilized and validated Meentemeyer’s equation [81]. The predicted and actual weight loss range in these experiments was between 8 and 38%, excluding an outlier of 92.6% due to termite activity. Based on these findings, the scoring system for this ESS assigns a maximum score of 100 for a 40% decay and a minimum score of 0 for 0% decay. Assuming a linear relationship between the ESS and the annual decay percentage, the scoring is represented by the following equation (to be detailed as Equation (10)):

$$x_{10}={\displaystyle \frac{weight loss (\%)}{40}}\times 100$$

(10)

Regulation of the Chemical Condition of Freshwaters by Living Processes (ESS₁₁)

This ecosystem service (ESS) related to the uptake of nitrogen and/or other chemicals by vegetation is specifically focused on preventing these chemicals from entering the freshwater system. The evaluation framework for this ESS is unique in that it does not consider the differing amounts of chemical pollution emitted by various land uses. Instead, it exclusively assesses the ESS based on the regulation of chemical balance, operating under the assumption that chemical pollution levels are uniform across all land uses.

Table 4. Scoring and measures for each LULC that improve the ESS 11.

LULC	Without Measures	With Measures	Type of Measure and Conditions for Improving ESS11
Forest	80	100	Long growing season [82] Low elevation [82] Tree species: different tree species respond differently to high nitrogen levels [82]
Agriculture	40	60	Buffer strips [83] Healthy soil microbial community [84] Adequate soil physical and chemical characteristics [85] Types of crops: leafy vegetables quickly absorb high amounts of nitrates [86] Adequate crop rotation system
Solar farm	0	20	Vegetation cover Buffer strips

represents the scoring system for this ESS, which reflects the ability of different land uses to uptake nitrogen and other chemicals. Forests are noted for their significant nitrogen uptake capabilities, which are attributed to their larger biomass, followed by agriculture. In contrast, the biomass found in areas such as solar farms can be as minimal as bare soil [27]. Furthermore, taking some measures, as described in the table below, improves the ESS across different land cover types. For instance, good agricultural practices would support the growth of buffer strips, which help in removing excess nutrients from runoff before it reaches freshwater bodies.

Groundwater Used as a Material (Non-Drinking Purposes) and Groundwater for Drinking (ESS_12–13)

Forecasting groundwater recharge and quality is a challenging task, as highlighted by [87], leading to the use of land use as a basis for scoring ESS 12–13 in this research. For ESS 12, which focuses on groundwater used as a material (non-drinking purposes), groundwater quantity is deemed a suitable indicator. This is because such usage typically requires large volumes of water where high quality is unnecessary. On the other hand, for ESS 13, which involves water used for drinking purposes, groundwater quality becomes a critical factor due to the challenges and costs associated with achieving drinking water standards.

Table 5. Scoring and selected examples of measures that increase ESS_12–13.

LULC	With Measures	Without Measures	Measures
Solar farm	100	80	buffer strips cover crops
Forest	60	40	sustainable harvesting riparian buffer zones invasive species management
Agriculture	20	0	conservation tillage crop rotation fertilizer and pesticide management irrigation management cover crops

illustrates the scores assigned to different land use and land cover (LULC) categories, comparing outcomes with and without the implementation of measures, while also specifying the measures applied to enhance sustainability and performance for each land-use type.

4.4. DSS and Its Application in Darstadt

We integrated the framework in a software program to facilitate the analysis by interested stakeholders. The opening interface upon launching the application is represented by the main window. This interface features a menu bar equipped with a drop-down list that provides language selection options. Positioned beneath the title and subtitle, the initial input from the user is requested through two boxes designated for entering the project name and area size. These inputs play a crucial role in organizational tasks and data exportation. At the middle of the window, a frame presents the results of ESS scores for the GPV and the alternative management strategies. Furthermore, a set of four push buttons are arranged vertically, featuring (i) a “start calculating Ecosystem Services” button that launches the evaluation window of the Aquasol tool, (ii) a “save result” button for exporting the findings, (iii) a “return to calculation” button that allows users to adjust input variables, and (iv) a “close App” button for exiting the application.

The DSS window includes 13 tabs. The first tab, depicted in Figure 4, facilitates the allocation of weights to each ecosystem service (ESS) based on their significance and necessity. This tab features a central frame housing twelve Spin Boxes, as shown in the figure titled “Input of weights”. It allows users to assign weights to each ESS within a scale from 0 to 1.

4.5. Application in the Darstadt Case Study

Based on the different methods discussed in the Framework Evaluation Section, the data of the Darstadt case study were integrated into the DSS tool via the DSS window Figure 5. This process involves calculating the final ecosystem service (ESS) scores for GPV and comparing them with an alternative management strategy identified for the Darstadt case study as an agricultural practice.

Table 6. ESS score results in the DSS tool for the Darstadt area.

Ecosystem Service No.	Details	GPV Score	Status Quo Score	Weights
1	Cultivated plants for nutrition	100	5.633	0.143
2	Cultivated plants for processing	-	-	0.0
3	Cultivated plants as energy source	-	-	0.0
4–6	Wild pants	-	-	0.0
7	Control of erosion rates	100	50	0.143
8	Mitigating mass movement	0	0	0.143
9	Hydrological cycle	80.8	64.4	0.143
10	Decomposition and fixing	99.998	84.565	0.143
11	Freshwater quality	20	40	0.143
12 and 13	Groundwater for drinking and processing	100	20	0.142
Results		71.51	37.82	1

depicts the final results comparing the ESS scores of the GPV and alternative practices in Darstadt. Several ESSs (ESSs 2–6) assign economic scores based on biomass that is not currently present on the agricultural site, leading to their weights being set to 0 in this evaluation. Given that the land cover is agricultural, the value assigned for ESS 8 is 0 for both the GPV and the alternative. The analysis reveals that GPV outperforms the alternative in all categories except for ESS 11, due to its reduced capabilities for nitrogen uptake by organisms. The comparison between GPV and current practices in the Darstadt case study indicates that GPV has a lower impact on soil erosion and nitrate leaching due to the presence of continuous ground cover. This finding is supported by studies [29,32] showing reduced soil displacement and improved groundwater quality in areas under GPV installations. However, we acknowledge that further empirical data from different climatic and soil conditions would strengthen this conclusion. The alternative agricultural strategy surpasses GPV, particularly in nitrogen uptake capabilities, affecting freshwater’s chemical status. Despite this, the overall advantages of GPV establish it as the preferred management practice over the alternative agricultural approach. And this is primarily attributed to the positive impacts of ecosystem services (ESS 1, ESS 7, ESS 9, ESS 10, ESS 12, and 13).

For instance, ESS7, the Universal Soil Loss Equation results, shows that the GPV mitigates soil loss and erosion more effectively than the alternative approach, which is vital for maintaining soil health. The impact of GPV installations on soil temperature, moisture, and erosion can vary significantly across different climatic zones. For instance, in arid regions, the shading effect of solar panels can reduce soil temperature and evaporation, enhancing soil moisture retention [17,53]. Conversely, in temperate regions, increased runoff due to altered precipitation patterns may elevate erosion risks [52]. Understanding these regional variations is essential for adapting the DSS tool’s assessment criteria accordingly. For ESS 9, both the GPV and the alternative strategies fall under hydrological soil group C [49]. The GPV uses continuous grass cover, while the alternative employs row crops in straight rows. The resulting hydrological cycle score are 80.8 for the GPV and 64.4 for the status quo. The ESS 10 score shows a higher score for the GPV, indicating that it has a better performance in enhancing decomposition processes; this is due to a presumed lower initial lignin concentration beneath the panels, attributed to the presence of grassland, compared to wheat.

It is important to note that these findings rely on the specific weights assigned and that some input parameters may require further investigation. A thorough stakeholder analysis involves consulting various stakeholders, such as local community members, environmental experts, and policymakers, to understand the relative importance of each ESS in the specific context of the study site. Due to the absence of a comprehensive stakeholder analysis, the balanced weights chosen for this evaluation serve as a starting point to avoid skewing the results in favor of any single ecosystem service and allow for a fair comparison between them. Further research and stakeholder engagement are necessary to refine these weights and ensure that they accurately reflect the local context and priorities. However, the Aquasol DSS tool successfully facilitates a comparative analysis of the final scores, validating its application in this context.

Stakeholder analysis and site-specific information are also fundamental to evaluating ESS 1–6. The revenue of solar parks and of alternative land uses can change over time due to fluctuations in market prices or changes in crops produced. Also, ESS7, related to soil erosion, can be assessed for different phases during the construction of the solar park or as average values over longer periods of time, depending on the purpose of the assessment. The purpose of the DSS is therefore not to provide a static and definitive answer to the question of whether the construction of a ground-mounted solar park is better than any other alternative but to enhance stakeholder discussion, awareness, and critical assessment of the complex interactions between solar parks and related ecosystem services. The proposed evaluation schemes could be made more complex (e.g., including hydrological and hydraulic modeling results) and more detailed (e.g., including the market value of different crops, crop rotation, and non-linear functions for point assignments). However, this could rapidly lead to a tool that could be of difficult use for non-experts and much too case-specific. Providing the software as open-source and open-access code, however, allows the interested user to customize and further develop it, by also including or removing specific ESSs and modifying assessment methodologies.

4.6. Limitations and Future Improvements

The DSS provides a framework that has been filled with preliminary values. The main value of the article is that a framework now exists that can assess the water-related ecosystem services (ESS) of open-field photovoltaic systems. While the DSS tool demonstrates efficacy in evaluating the impacts of solar park constructions on water-related ESSs, certain limitations must be acknowledged. These include the reliance on time-independent input parameters, the limited integration of dynamic hydrological models, and the exclusion of co-location strategies such as agrovoltaics. To improve the DSS, future versions could focus on incorporating real-time data feeds, enhancing model dynamics through hydrological simulations, and integrating multi-objective optimization techniques. Additionally, considering a Life Cycle Assessment (LCA) approach could provide a more holistic understanding of the environmental impacts of solar parks. Integrating LCA results into the DSS tool could support more comprehensive decision making by quantifying trade-offs between energy production and environmental impacts over the entire lifecycle of PV systems. To mitigate the limitations in modeling hydrological processes, future versions of the DSS tool could incorporate results of dynamic hydrological models, such as SWAT [88] or MIKE SHE [89] to simulate water flow and quality under different land-use scenarios. Additionally, expanding the range of ecosystem services considered and including user-friendly customization options will further enhance the tool’s applicability and accuracy.

5. Conclusions

This research was conducted to investigate the multifaceted impact of ground-mounted photovoltaic systems (solar parks) on water-related ecosystem services (ESSs). The developed evaluation framework, based on an extensive literature review and expert opinions, is intended to provide a comprehensive tool for analyzing interactions between solar park construction and water-related ESSs. This study highlights the significant influence of solar parks on various components of the hydrological cycle and, subsequently, on ESSs such as soil moisture regulation, erosion control, and water quality. By applying the Common International Classification of Ecosystem Services (CICES) and a multi-criteria decision-making approach, this research contributes to an in-depth insight into the environmental implications that would be considered during the installation of a new solar park, and it underscores the importance of considering these impacts in planning and managing solar energy projects to promote site-specific management practices that minimize adverse impacts on water-related ecosystem services (ESSs).

The evaluation framework transformed into a user-friendly interface, presenting a platform for entering parameters, featuring well-defined objects, facilitating access to information via dialogues, and offering clear and exportable outcomes. The tool has been applied to Darstadt, Germany, featured its effectiveness, with findings suggesting that the adverse effects of GPV on water-related ecosystem services are considerably less than those associated with status quo management strategies. However, due to the complexity of hydrological processes, it is acknowledged that this study faces limitations and the inherent simplifications required in modeling these systems. An expanded analysis with regard to climates and land uses and the incorporation of emerging co-location strategies in solar farms may be considered in further research. Additionally, exploring diverse literature databases and including more comprehensive ESSs will further enhance the understanding of the ecological impacts of solar parks.

Furthermore, the developed framework is designed to be adaptable to different geographical contexts and land-use scenarios beyond the Darstadt case study. By including additional ecosystem services (ESSs) relevant to different climatic and socio-economic settings, the framework can support decision making for solar park development in various regions globally. Furthermore, additional enhancements of the DSS tool should focus on a closer examination of input data and parameters for more robust decision making. This includes plans to update the framework and software from CICES V5.1 to CICES V5.2 and to integrate the tool into a GIS environment, which could significantly improve its application and accuracy. Additionally, incorporating hydrological modeling would allow us to quantify the impact of different solar park configurations, minimizing negative impacts on water-related ESSs and maximizing benefits. These enhancements will expand the framework’s applicability by incorporating regional-specific data and refining the DSS to accommodate a wider range of environmental conditions.