Digital Platforms for Climate-Resilient and Sustainable Planning: Lessons on Nature-Based Solutions from a Louisiana Watershed-Scale Case Study

Abstract

Digital platforms have been increasingly adopted to support sustainable climate-resilient planning by implementing nature-based solutions (NbSs) as an effective short-term strategy. Although existing studies have deepened the operational performance of digital platforms, less attention has been paid to their role as knowledge infrastructure for shaping sustainability-relevant planning practices. This paper examines the informative structure of the Louisiana Watershed Initiative (LWI) platform. This is intended as a relevant case study to investigate how digital platforms organize data, information, and knowledge to support NbS-oriented climate resilience at the watershed scale. The study adopts a mixed-method case-study approach, combining an interpretative analysis of the platform’s digital and informational architecture with targeted tests of NbS-oriented decision-support interfaces. The results highlight the operational and cognitive conditions in shaping NbS prioritization processes—notably, those related to scaling, informational structuring, and governance alignment. While the platform effectively supports digital decision-making processes at regional and watershed levels, limitations emerge regarding how ecological knowledge is produced, interpreted, and operationalized within planning frameworks, with implications for the long-term sustainability and robustness of planning decisions. The lesson learnt by the analysis of the LWI identifies the conditions under which the analytical approach can be replicated and highlights insights relevant to both the design and evaluation of digital decision-support platforms in NbS-oriented planning contexts.

1. Introduction

Nature-based solutions (NbSs) are increasingly promoted in environmental planning and design as multifunctional strategies capable of addressing climate-resilient strategies while delivering ecosystem services and related co-benefits [1,2,3,4,5,6]. Their capacity to regulate hydrological processes, moderate microclimates, support biodiversity, and improve environmental quality has been widely documented, together with social and cultural benefits, particularly in urban contexts [7,8,9,10,11].

However, despite this consolidated evidence, persistent challenges arise when NbS design principles are translated into spatially explicit and operational planning decisions, particularly due to the inherently both multi-scalar and interdisciplinary nature of NbS implementation. In line with these remarks, the research challenge concerns how different analytical approaches are articulated within digital platforms to support the localization and prioritization of NbSs across scales. Notably, research aims to investigate how different disciplinary branches of knowledge can be taken together, to influence the ways in which ecological conditions, spatial information, and decision criteria interact. In fact, NbSs are often framed through broad conceptual categories, heterogeneous indicators, and context-dependent performance assumptions, which limit their effective integration into decision-making processes, as also reflected in recent European regulations on soil management, land use, and ecosystem restoration [12,13,14,15,16,17,18].

Within this context, the need to translate NbS principles into spatially explicit and comparable planning decisions has led to an increasing reliance on digital decision-support tools. Spatial Decision-Support Systems (SDSSs) are used to organize spatial, ecological, and socio-economic information in supporting site selection, scenario analysis, and resource allocation in climate-resilient planning [19,20,21].

The shift toward fully digital platforms extends traditional, sectoral SDSSs into interactive environments, enhancing the visualization and interpretation of complex datasets through accessible interfaces [22]. In these scenarios, digital interfaces play a key role in terms of cognitive and operational processes, mediating the transformation of heterogeneous data into structured, decision-oriented knowledge [23]. By integrating multiple data sources—including satellite imagery, aerial data from drones, ground-based sensor networks, and IoT networks—a major awareness of NbS opportunities can be developed. The incorporation of citizen science contributions can further validate broad data interpretation and participation, promoting more participative decision processes [24].

While digital platforms are increasingly adopted to support the localization and prioritization of NbSs, prevailing analytical approaches continue to emphasize data availability and computational performance. SDSSs’ role as knowledge infrastructure [25] are often underestimated. In fact, they can be interpreted as socio-technical systems that actively shape how knowledge is produced, interpreted, and operationalized within planning frameworks, beyond data availability or quality [26]. These platforms embed specific logic that structures the relationships between ecological evidence, spatial criteria, and planning objectives, rather than being neutral repositories of information. In this light, decisions concerning the location, scale, and comparability of NbS interventions are not solely determined by the datasets in use, but also by how information is organized, connected, and sequenced within the digital tools themselves. Despite the huge capacity in data management, the SDSS structuring principles often remain implicit or analytically unaddressed, so that there is the risk of narrowing interpretative flexibility, limiting the adaptability of NbS strategies in different socio-ecological and institutional contexts.

This study addresses this gap by examining the Louisiana Watershed Initiative (LWI) digital platform as a case study to clarify how NbS options are framed, evaluated, and justified through the decision process embedded in its decision-support environment. The structured analysis of the platform provides lessons on how digital decision-support platforms operate as knowledge infrastructure that actively shapes NbS prioritization and spatial decision-making under climate risk scenarios. This analysis is further used to identify the conditions under which this experience can be replicated in designing NbS-oriented digital platforms across different territorial and institutional contexts.

To do so, this paper contributes to the broader debate on digital decision-support for NbS-oriented climate-resilient planning by advancing an analytical framework for understanding how digital tools influence planning choices. To address this objective, this study adopts the DIKW approach [27,28] as an interpretative lens to analyze the LWI digital decision-support platform.

Beyond the introduction, this paper is structured in four sections. First, Section 2 develops the methodological framework adopted for analyzing LWI digital platforms as knowledge infrastructure. The methodology also includes the analytical testing of platform usability through the empirical examination of digital interfaces, applied to the watershed scale. Section 3 presents the results of the analysis, coming from the application of the DIKW framework to the specific features of the LWI platform. Section 4 critically discusses the results, pointing out the conditions under which the analytical approach can be replicated. In particular, the discussion highlights relevant insights in terms of both design and digital support processes in NbS-oriented planning contexts. Finally, the concluding section summarizes the main findings and outlines how the results open opportunities for future research.

This study is developed within the Marie Skłodowska-Curie Research and Innovation Staff Exchange (RISE) project TREND—Transition with Resilience for Evolutionary Development (Grant Agreement No. 823952), which provides the institutional and territorial framework for examining decision-support practices in climate-vulnerable contexts.

2. Materials and Methods

2.1. Preliminaries and Contextual Framing

Digital decision-support platforms for climate resilience are developed and deployed within specific territorial and institutional settings that shape their scope, structure, and operative logic. For this reason, the context in which a platform operates provides a necessary reference for understanding how ecological information is processed and translated within decision-making processes.

In Europe, digital platforms are often embedded within policy-driven adaptation strategies, prioritizing urban-scale climate governance and knowledge-sharing frameworks. The Climate-ADAPT platform (European Environment Agency, Copenhagen, Denmark) [29] exemplifies this approach, providing structured datasets, adaptation case studies, and interactive risk assessment tools that support decision-making at multiple governance levels. The Digital Twin for Climate Resilience (European Commission and European Centre for Medium-Range Weather Forecasts, Reading, UK) [30] further illustrates advanced simulation-based approaches for infrastructural decision-making, emphasizing the role of digital environments in supporting coordinated planning and comparative evaluation across sectors.

In the United States, the integration of NbSs is closely associated with watershed-scale flood risk management and with planning frameworks that rely on hydrological modeling, ecosystem service assessment, and natural capital accounting [31,32]. Compared to policy-oriented European frameworks, the US approach places greater emphasis on large-scale spatial coordination, predictive capacity, and the alignment of investments across jurisdictions [33,34,35,36].

Within the US context, the LWI (State of Louisiana, Baton Rouge, LA, USA) constitutes a particularly relevant case study.

The LWI was established in 2016 due to a series of severe flood events. It was conceived as a main part of a statewide, watershed-based program to overcome the limitations of fragmented flood mitigation practices [37,38]. This initiative introduced an integrated planning framework that aligns flood risk reduction, ecosystem-based strategies, and investment prioritization across hydrological boundaries. Such an ambitious goal was supported by a centralized digital decision-support platform.

LWI was developed to coordinate multiple state agencies and local authorities, integrating heterogeneous spatial and environmental data and supporting the selection of NbSs within climate-resilient strategies [39]. This setting has enabled shared evaluation logic through operationalized scenario assessments. These work on common metrics and relevant learning processes at the watershed scale.

The selection of the LWI as a case study is motivated by the following criteria:

• Relevance of the territorial context (Figure 1). Louisiana offers a relevant combination of environmental complexity, exposure to natural hazards, planning challenges, low-density human settlement, and active public–private partnerships [40].
• Relevance of the topic. Flooding was selected as the primary hazard due to its global significance and comparative value. This enables the analysis of adaptation strategies and digital platforms, addressing a common, urgent challenge in international contexts, despite differences in ecosystem service approaches.
• Data availability. The LWI platform provides access to heterogeneous data sources, including census data, satellite imagery, georeferenced maps, and field surveys.
• Technological innovation. The LWI platform features an advanced technological and information structure. These features support potential transferability and broader application to other contexts.
• Usability. The LWI platform integrates decision-support tools designed for different users and purposes, adopting differentiated user interfaces aligned with specific user roles and decision needs.

2.2. Methodological Approach

This study adopts an analytical case study approach to investigate how digital decision-support platforms operationalize ecological knowledge in supporting NbS planning and design processes for climate resilience. The analysis focuses on the internal informational structure of the digital platform features, with special attention to how data are organized, processed, and translated into decision-support outputs.

In recent years, various research perspectives have addressed how digital platforms shape the production and use of knowledge in environmental planning, particularly in relation to data infrastructures and decision-making processes. These contributions have highlighted the interplay between a platform’s technical structure, institutional frameworks, and epistemic assumptions. For example, Science and Technology Studies (STS) have critically examined how knowledge is mediated through embedded practices and relational configurations [41].

However, its focus on socio-material assemblages offers limited operational means for analyzing how digital systems internally structure and transform data. By contrast, the Knowledge Governance lens investigates the regulatory and institutional conditions under which knowledge itself gains legitimacy [42] but does not directly question the informational structure embedded into decision-support tools.

Similarly, the lens of epistemic infrastructures highlights the role of technical standards and distributed networks of social actors [43]. Despite its usefulness, its high level of abstraction makes it less suitable for analyzing digital processes of decision-making platforms.

To engage more directly with the informational dimension, this study adopts the DIKW (Data–Information–Knowledge–Wisdom) framework [28] as an interpretative lens. This analytical framework was originally formulated by Ackoff [27] and widely consolidated within the information science and decision-support systems literature.

DIKW enables a structured analysis of digital infrastructures as layered information systems. It supports a systematic examination of heterogeneous data sources and analytical components. In the DIKW lens, the interface-level outputs are integrated to produce actionable knowledge within SDSSs.

The DIKW analytical framework is applied here to reconstruct how the platform processes ecological data from raw inputs to user-facing decision-support functions. This is achieved through a systematic analysis of technical documentation, user manuals, datasets, and decision-support tools. Each informational layer—data, information, knowledge—is examined to trace the internal transformation logic underpinning the platform’s outputs.

The methodological approach is structured into three complementary phases (Figure 2):

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Phase 1 consists of a DIKW-based analysis of the platform’s digital infrastructure. This phase is aimed at understanding how data, information, and knowledge are structured and interlinked within the system.

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Phase 2 focuses on the testing of NbS-oriented decision-support tools. This phase specifically examines how the informational structures selected in Phase 1 are operationalized through the platform’s user interface, as experienced by the authors acting as expert users.

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Phase 3 builds on the analytical insights from the previous phases. This phase aims to identify the key informational structures and decision-support processes for non-expert users. This phase defines the main topics for designing a questionnaire to be administered to a targeted user group in the future stage of the research.

Phase 3 is included here to provide a comprehensive overview of the research design. However, this paper presents the results related to Phase 1 and Phase 2 only.

2.3. Phase 1—DIKW-Based Analysis of the Digital Infrastructure

Phase 1 applies the DIKW analytical framework [27,28] to analyze the informational architecture of the LWI platform through a systematic classification of the resources published across its programs (Appendix A). This analysis involves the collection of all publicly accessible and operational materials available on the LWI official platform, including datasets, interactive maps and dashboards, policy and program documents, methodological reports, training materials, webinars, and application portals.

Each resource is classified according to its functional role within the platform, following established definitions in the information science and decision-support systems literature (Appendix A).

In line with Ackoff’s formulation and subsequent developments, the DIKW components are defined as follows:

• Data, here identified as raw or minimally processed measurements and observations (e.g., geospatial layers, monitoring records, dashboards displaying values);
• Information, referring to resources that organize, structure, or contextualize data through rules, plans, criteria, or policies;
• Knowledge, intended as interpretative and procedural resources that explain how information is generated and how it can be applied in planning and decision-making contexts (e.g., the specific programs in the LWI);
• Wisdom is not treated as a directly observable layer of the digital infrastructure, due to it being an empirical dimension associated with the use of the platform in decision-making contexts.

This analytical process results in a program–resource mapping, synthesized in Appendix A.

For each resource, Appendix A is organized in six columns:

• The program or initiative in which the resource is embedded (first column). The programs are here interpreted as the Knowledge level of the analysis (i.e., the context that guides the interpretation and application of information).
• The classification of the resources as Data or Information, based on its level of structuring and interpretability.
• A description of its function and purpose.
• The authorship of resources.
• Targeted users, categorized across governance, technical, educational, or stakeholder domains.
• The rationale—explicit or implicit—underlying its classification within the DIKW analytical framework.

This structured classification is used to reconstruct the information processing sequence. In Appendix A, each informational layer of the platform is mapped individually. This approach allows a structured reading of interactions across DIKW levels, focusing on the transparency, the coordination among institutional actors, and scalability across environmental and governance contexts.

2.4. Phase 2—Analysis and Testing of NbS-Oriented Decision-Support Interfaces

The second phase focuses on testing the platform digital tools explicitly dedicated to NbSs. Within the LWI platform, the NbS Program provides a broad range of resources and strategic frameworks for environmental management. Among these, two specific decision-support tools were selected for the analysis. This phase examines how information is structured, translated, and operationalized through decision-support interfaces. The tests especially focus on the configuration of indicators, spatial criteria, and user interaction mechanisms.

The first tool is the NbS Explorer Tool (RTI International, Research Triangle Park, NC, USA; The Nature Conservancy, Arlington, VA, USA) The tool is designed on a watershed-based scale that ranges from regional to catchment scale units and supports the prioritization of NbS interventions (Figure 3). Due to its current commercial status, the tool was not directly tested by the authors. The study analyzes User Interface through a structured review of official technical documentation, user manuals, and methodological reports (Appendix A). While the first phase analyzes the whole digital resources, Phase 2 focuses on the ones related to ecosystem service indicators, hydrological risk parameters and socio-economic variables.

The second tool is the Opportunity Map Viewer (RTI International, Research Triangle Park, NC, USA; The Nature Conservancy, Arlington, VA, USA) an open-access ArcGIS-based web interface (Esri, Redlands, CA, USA). The testing activity examined how users can interact with spatial datasets—such as land cover, flood exposure, and ecological indicators—to explore NbS suitability through layered visualization and spatial comparison. The testing of Opportunity Map Viewer is done directly by the authors, serving as expert users.

Both tools were tested through four analytical dimensions to comply with the DIKW analysis developed in Phase 1.

This approach addresses a comparative reading between the platform’s declared informational structure and its functioning when tools are accessed and used.

The four analytical dimensions are as follows:

(I).

Indicators and key ecosystemic metrics, examining how ecosystem services, flood risk, and socio-environmental conditions are selected, represented, and operationalized within the tools;

(II).

Spatial criteria for suitability, focusing on how spatial datasets are combined, filtered, and visualized to support the localization and comparison of NbS planning;

(III).

Interface configuration and interaction, testing how users can navigate, interpret, and interrogate NbS-related information through the LWI interfaces;

(IV).

Decision-support orientation, analyzing how tools support prioritization processes, according to their specific outputs.

2.5. Phase 3—Usability Validation

Phase 3 refers to the usability validation. This phase constitutes a preparatory step within the overall methodological framework. It builds on the analytical results from Phase 1 and Phase 2 and aims to reorganize and translate these findings into a structured format suitable for future empirical investigation. The goal is to define a coherent set of variables that can inform the design of a questionnaire, targeting diverse user profiles—including non-expert users—and oriented toward assessing the platform’s perceived utility and comprehensibility.

In accordance with the ISO 9241-11 definition [44], this study intends the term usability as the degree to which a system enables specified users to achieve defined goals effectively, efficiently, and with satisfaction, within a given context of use. In this phase, the concept is operationalized through the identification of key informational elements and interaction features that shape the user experience.

The output of Phase 3 consists of a preliminary questionnaire framework (Appendix B), organized into thematic sections. This guides the LWI platform comprehension, the interaction with digital tools, and the perceived relevance for planning and decision-making. The questionnaire marks the methodological transition from analytical decomposition to the future empirical validation of user experience and tool effectiveness. The further development of the research will be done thought the use of the questionnaire in dedicated surveys.

3. Results

3.1. Phase 1—Findings from DIKW-Based Analysis of the Digital Infrastructure

The analysis reconstructs, through the DIKW interpretative process [27,28], how heterogeneous datasets, documents, and digital resources are progressively organized and translated into decision-support functions for climate-resilient planning (Appendix A). The resulting structure (Figure 4) depicts the informational chain through which the digital platform organizes ecological, socio-economic, and institutional knowledge.

• Data. At the Data level, the platform aggregates raw, spatially explicit datasets that describe the physical, ecological, and socio-economic conditions relevant to flood risk management and NbS planning. These include hydrological records and infrastructure data—such as flood hazard maps, historical flood events, rainfall and streamflow records, and hydraulic infrastructure inventories—which provide baseline metrics for understanding flood dynamics and system capacity. Land-use and socio-economic datasets, including land-use/land-cover classifications, socio-economic vulnerability indices, and impervious surface data, define the human and territorial context within which resilience strategies are formulated. Vegetation and ecological datasets, such as the Mean Vegetation Condition Index and biodiversity layers derived from regional conservation frameworks, support the identification of ecosystem conditions relevant to NbS opportunity mapping. A comprehensive list of the key spatial indicators and data sources used in this study is presented in

  Table 1. Key spatial indicators and data sources for NbS programs within LWI platform.

  Indicator	Description	Measurement Unit	Dataset	Dataset Source	Purpose
  Land-Cover Change	Change in natural vegetation (wetlands, forest, prairie) to developed/agricultural land.	% Area Changed	NLCD 2001 & NLCD 2019	US Geological Survey (USGS)	Identify suitable restoration areas
  Vegetation Condition (MVCI)	Deviation of vegetation from historical normal conditions.	% Deviation	VegScape MVCI	USDA, National Agricultural Statistics Service	Detect vegetation health & degradation
  Impervious Surface	Extent of impervious surfaces projected to 2050.	% Impervious	ICLUS Impervious Coverage	US EPA, Office of Research and Development	Identify urbanization pressures
  Floodplain Area	Area susceptible to 100-year flood inundation (fluvial/pluvial).	Area (km2)	Fathom US Flood Map	Fathom Global	Define floodplain boundaries
  Riparian Area	Land adjacent to rivers identified by 50-year flood heights.	Area (km2)	USFS Riparian Area Map	US Forest Service (USFS)	Define riparian restoration/preservation
  Catchment Delineation	Small hydrological catchment units for analysis.	Area (~2.59 km2)	NHDPlus v2.1	US Geological Survey (USGS)	Basic unit for spatial analysis
  Soil Hydrologic Group	Soil infiltration capacity influencing runoff.	Categories (A–D)	SSURGO	USDA-NRCS	Evaluate suitability for infiltration
  Slope	Mean slope within catchments/developed areas.	% slope	1/3 arc-second DEM	USGS	Evaluate runoff & erosion risk
  Stream Sinuosity & Slope	Measures stream meandering and slope for restoration suitability.	Ratio & %	NHDPlusV2, TNC Python Script	USGS, The Nature Conservancy (TNC)	Identify stream restoration potential
  Protected Areas	Already conserved land.	Area (km2)	PAD-US	US Geological Survey (USGS)	Exclude areas from conservation planning
  Urbanization Component	Composite urban disturbance index (Percent high intensity development, Percent medium intensity development, Percent low intensity development, Percent impervious surfaces, Percent open space, Road density, Population density).	Principal Component	StreamCat EPA, TIGER 2020, Census	US EPA, US Census Bureau	Urbanization assessment & suitability
  Urban Park Size	Parks larger than 5 acres in urban environment.	Area (acres)	SECAS Urban Park Size	Southeast Conservation Adaptation Strategy (SECAS)	Identify suitable stormwater parks
  Wetland Migration Corridors	Potential inland migration areas for coastal wetlands.	Area (km2)	Gulf of Mexico Migration Space	The Nature Conservancy (TNC)	Identify wetland migration corridors
  Agricultural Land & Crop Type	Agricultural land use and crop types for land management.	Crop type/area (%)	USDA Cropland Data Layer	USDA, National Agricultural Statistics Service	Manage working lands

  , grouped by type and aligned with their analytical role within the platform.
• Information. At the Information level, raw datasets are structured, contextualized, and made accessible through inventories, guidelines, and technical resources. The LWI Regional Project Inventory consolidates project descriptions, funding sources, and benefit assessments into standardized records, enabling comparison across interventions and regions. Training materials, modeling guidelines, and methodological resources—such as the PRO Louisiana courses—translate data into operational workflows, while watershed-specific guidance documents contextualize information within local hydrological and institutional settings. At this level, data are no longer isolated inputs but become organized references for planning and coordination.
• Knowledge. The Knowledge level corresponds to the integration of structured information into operational programs and coordinated planning actions. State-led projects and programs draw on the information layer to prioritize investments, align inter-agency initiatives, and monitor implementation. Capacity-building processes, including regional grant programs, use platform-generated insights to enable local and regional governance. Within this level, the Nature-Based Solutions Program plays a central role, integrating ecosystem service indicators, hydrological modeling outputs, and socio-economic metrics through dedicated digital tools and training modules. Here, aggregated information is translated into site-specific, evidence-based options for NbS localization and design.
• Wisdom. In this study, there are no results for the Wisdom level. This is not represented as a discrete or directly observable layer of the platform because it is an emergent outcome generated by specific users within governance and decision-making contexts. This dimension could be analyzed in future studies by simulating the whole planning decision process.

Across the examination levels, two complementary pathways emerge. The first pathway is the fully digitized resources and tools, as outlined in Figure 4 (black lines). This pathway is accessible through dedicated user interfaces such as dashboards, interactive maps, and explorer tools, enabling dynamic interaction and real-time updating.

The second pathway is qualitative and semi-static resources, as outlined in Figure 4 (colored lines). This pathway includes guidelines, compendia, and technical documentation, providing contextual interpretation and methodological grounding. Both these pathways constitute the informational infrastructure through which the LWI platform supports NbS-oriented decision-making.

3.2. Phase 2—Mapping Opportunities for NbSs with Digital Interfaces

The results of Phase 2 are organized into three blocks. The first provides the workflow of the two tools tested by the authors, highlighting key steps for the decision-making process. The second investigates more specifically the analytical aspects of the two workflows. The third focuses on the interfaces of the two tools with respect to their own level of usability.

The testing of the NbS-oriented tools of the LWI digital platform depicts how decision-support interfaces provide the localization, comparison, and prioritization of NbSs. The NbS Explorer Tool and the Opportunity Map Viewer have a specific decision logic that selectively combines ecological, hydrological, and socio-economic variables and makes them operational for planning purposes.

Both tools rely on a catchment-based data architecture and standardized national datasets—including NLCD, USFS Riparian Areas, and Fathom Flood Maps—thereby establishing the watershed and sub-catchment as the primary spatial frame for NbS prioritization (Figure 3). Despite differences in accessibility and interaction modes, the two interfaces share a common analytical structure to evidence NbS planning opportunities (Figure 5).

Specifically, Figure 5 illustrates how the two tools operationalize the same workflow through differentiated user pathways and levels of functionality. While both tools are conceptually aligned with the two-stage process of NbS opportunity identification and project evaluation, they emphasize distinct phases of this workflow:

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The Opportunity Map Viewer primarily supports Pathway 1 (Stages 1–5), focusing on the preliminary identification of NbS opportunities. Through an open-access and simplified interface, users visualize statewide and catchment-scale NbS opportunity maps, navigate areas of interest, and explore land cover, hydrological boundaries, and ecological suitability layers. This tool is designed to facilitate an early-stage exploration and pre-selection of potential areas. This pathway is mainly oriented to non-expert users and to preliminary planning or meta-design purposes.

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The NbS Explorer Tool extends this logic by providing additional functionalities associated with Pathway 2 (Stages 6–9). In addition to NbS opportunity mapping (stage 1–5), it enables the configuration and evaluation of specific NbS planning strategies. This second pathway allows expert users to select NbS types, define project parameters, and generate scenario-based outputs, including socio-economical ones. Through integrated metrics and on-demand modeling, the NbS Explorer Tool supports impact estimation and the export of structured results, such as maps and performance summaries, oriented toward more advanced planning and decision-support needs.

This two-path structure aims to balance the platform’s analytical ground (Phase 1) with an interactive visualization of NbS planning opportunities (Phase 2), so that both expert users and non-institutional stakeholders can use the platform at different levels.

3.2.1. Analytical Framework for NbS Prioritization

Although the two tools operationalize the workflow through differentiated user pathways (Figure 5), the information is structured around a shared analytical framework in both interfaces.

This analytical framework for NbS prioritization is grounded in three interrelated analytical dimensions, defined through indicators, metrics, and criteria synthesized from the platform’s technical documentation (Phase 1—Appendix A). Together, these dimensions structure how ecological suitability, flood risk, and socio-territorial conditions are combined and translated into NbS opportunity mapping and prioritization. The three analytical dimensions are based on the following:

• Ecosystem-service indicators are used to characterize ecological condition and functionality. Vegetation condition is assessed through the Mean Vegetation Condition Index (MVCI) derived from the VegScape platform, enabling the identification of degraded areas (MVCI ≤ −5%) and well-performing ecosystems (MVCI ≥ 5%), thus supporting both restoration and preservation strategies. Land-cover dynamics are mapped using the National Land Cover Database (NLCD 2001 and 2019). This map allows the detection of transitions between natural, agricultural, and developed land uses. Riparian and floodplain functions are mapped through the USFS National Riparian Areas Base Map and Fathom 100-year floodplain datasets, while soil and geomorphological characteristics are derived from SSURGO data and DEM-based metrics, including slope and stream sinuosity. All these indices and datasets are summarized in Table 1.
• Risk assessment criteria refer to ecological conditions on flood hazard dynamics. Flood susceptibility is assessed using high-resolution Fathom Flood Maps for both fluvial and pluvial flooding. Hydrologic connectivity is assessed through stream order, catchment boundaries (NHDPlus), and the spatial overlap between floodplains and riparian areas. Future exposure is incorporated through ICLUS land-use and urbanization scenarios, integrating long-term development into current NbS planning processes.
• Socio-economic metrics refer to environmental and risk indicators, situating NbS planning opportunities within their territorial and institutional context. Social vulnerability is identified through the spatial overlapping of income, population density, and low- to moderate-income community layers. A special indicator is the Urban Principal Component, derived via PCA from variables such as development intensity, road density, impervious surfaces, and population density. This is used to distinguish urban and peri-urban contexts. Land ownership and protection status are accounted for through the PAD-US dataset, excluding already protected areas from prioritization.

3.2.2. User Interface Configuration for Non-Expert Users

The NbS-oriented tools in the LWI platform are implemented through a web-based interface designed to account for different levels of user expertise and interaction. Figure 6 compares the two main decision-support interfaces. The Opportunity Map Viewer (top left) and the NbS Explorer Tool (bottom left) reflect distinct degrees of functional complexity and user engagement.

The Opportunity Map Viewer is specifically designed for non-expert users, providing a simplified interface that supports exploratory interaction using predefined NbS categories and suitability layers. Through basic GIS queries and layer activation, users can identify areas potentially suitable for different NbS types—such as restoration, preservation, or green infrastructure—without requiring advanced technical knowledge. Interaction is primarily based on visual interrogation and filtering, allowing intuitive navigation across land-cover, flood exposure, and ecological suitability datasets.

By contrast, the NbS Explorer Tool targets expert and institutional users, offering a more advanced set of functionalities for project-oriented analysis. This interface enables users to move beyond opportunity screening toward project configuration and evaluation, including the selection of NbS types, definition of project parameters, and exploration of performance metrics. The higher level of interaction reflects its role in supporting technical assessment, scenario comparison, and investment-oriented decision-making.

Figure 7 illustrates a typical output of the Opportunity Map Viewer, highlighting how simplified interaction is translated into information for non-expert users. The map output classifies territorial units according to their suitability for different NbS practices, based on user-selected criteria within the GIS interface.

Table 2. Inventory of NbS types and operational practices as provided by the LWI platform.

Ecosystem Approach	Description by LWI	NbS Strategies
Restoration	The rehabilitation of degraded natural lands or channels, or the re-establishment of land cover, removal of artificial drainage features, and restoration of natural contours so that soils, hydrology, vegetative community, and habitat are a close approximation of the original natural condition that existed prior to modification.	Floodplain Restoration Riparian Vegetation Restoration Natural Channel Design Wetland, Prairie, Forest Restoration
Preservation	Actions to preserve existing natural conditions across the landscape to continue to benefit from the hydrologic functions (infiltration, evapotranspiration, and water storage) occurring within unmodified geomorphology, soils, and vegetative cover.	Floodplain Preservation Riparian Vegetation Preservation Open Space Preservation Preservation of Natural Lands
Protection of Wetland Migration Corridors	Protection of inland areas identified as potential locations for wetland migration due to changes in coastal dynamics related to land subsidence and potential sea-level rise. Actions may include combinations of preservation, restoration, and/or protection given current land cover and ownership status.	Protection of Wetland Migration Corridors
Management of Working Lands	Adjustments in agriculture, forestry, or other land-management practices to improve infiltration and evapotranspiration and/or hold water in the landscape.	Variety of agricultural best-management practices, land-cover changes, or drainage modifications, dependent on land characteristics
Green Infrastructure	Range of measures that use plant or soil systems; permeable pavement or other permeable surfaces or substrates; stormwater harvesting and reuse; landscaping or rewilding to store, infiltrate, or evapotranspire stormwater and reduce flows to sewer systems or to surface waters.	Green Infrastructure (bioretention, tree trench, infiltration trench) Stormwater Park (subsurface storage, stormwater detention basin, constructed wetland)

extensively reports the NbS types and practices available within the LWI NbS Program, directly linked to detailed technical documentation. These linkages allow users to move from the spatial identification of opportunity areas to the consultation of standardized descriptions of intervention types (Table 2).

4. Discussion

The findings of this study underscore both the operational effectiveness and epistemic implications of the LWI digital framework in supporting the NbS planning and design. Results demonstrate the platform’s capacity to integrate multi-criteria assessments and spatial analyses. The digital support system of LWI enables the identification of priority areas and facilitates evidence-based decision-making processes, particularly at the regional and watershed scales. The application of the DIKW analytical framework confirms the role of the LWI platform as knowledge digital infrastructure [25,26].

However, the analysis also reveals structural limitations in the platform’s internal logic. The transition from the Data level to the Knowledge one is mostly linear and unidirectional, revealing a kind of deterministic logic. The lack of iterative mechanisms within the levels of the DIKW framework—such as real-time data updating, scenario feedback, or adaptive processing—reduces the system’s responsiveness to adapting planning to different conditions and local priorities. The platform’s adaptability and epistemic openness could be enhanced by integrating citizen science contributions and feedback loops [24].

From an institutional standpoint, the LWI platform operates within a centralized governance architecture, despite being designed for multi-actor engagement. Data interoperability and inter-agency coordination are improved by digital interfaces, yet barriers remain in terms of technical accessibility and stakeholder inclusiveness. These constraints limit the platform’s potential to support more participatory and context-sensitive NbS implementation.

The testing of the NbS Tools (Section 3.2) confirms its effectiveness in mapping priority areas for flood mitigation and ecosystem restoration at the watershed scale. Its structured criteria and visualization functions offer a repeatable model for NbS planning. Nevertheless, the tool has limited applicability in urban and peri-urban contexts. The testing phase shows a gap between the tool’s spatial resolution and the level of information required for local NbS planning. As a result, higher-resolution data, locally defined typologies, and more suitable urban indicators are required to effectively support the implementation of NbSs in urban areas.

5. Conclusions

The lesson learnt by the LWI analysis identifies a set of design settings apt to provide the repeatability of the experience in other territorial and institutional contexts (Table 1 and Table 2). The results validate the DIKW analytical framework as adequate lens to deepen the embedded structure of the LWI platform. Figure 5 explicates the platform’s operational pathways as a multilayer cognitive system.

The analysis of the LWI digital design rationale (Figure 4) points out that digital tools are adept at handling multiple layers of ecological, social, and institutional complexity.

The results also indicate that the effectiveness of such digital platforms depends less on technical performance, and more on their ability to accommodate diverse knowledge domains, support iterative processes, and align with the socio-political and regulatory context in which they operate. The results also highlight the main constraints coming from the LWI platform’s analysis. First, context-driven adaptability is essential: digital tools need to be designed so that their structure, indicators, and data inputs can be reconfigured in relation to the specificities of each application context. Second, procedural transparency is necessary for enabling the iterative validation of results, especially when platforms are used for decision that affects public policy and resource. Third, the integration of local, institutional, and scientific knowledge is a crucial dimension for the operational effectiveness of NbS-oriented digital tools.

These constraints become particularly evident with respect to the contemporary policy framework that explicitly requires large-scale planning decisions and the implementation of coordinated NbS planning for reducing climate risks. For instance, recent European strategies place ecosystem restoration at the core of climate resilience and public well-being agendas [12,13,14,15,16,17]. The Nature Restoration Law [18], in particular, establishes binding targets for Member States, requiring restoration measures on at least 20% of the Union’s land and sea areas by 2030 and across all ecosystems in need of restoration by 2050. These objectives imply planning and design approaches capable of operationalizing ecological processes within viable territorial decisions, while simultaneously addressing climate change and biodiversity challenges.

Further, the transferability of the LWI framework to other territorial contexts depends on multiple structural factors, including data regimes, governance constraints, and institutional capacity. Its reliance on high-resolution geospatial data and state-managed hydrological baselines may hinder applicability in regions with fragmented data systems or limited environmental data access. To ensure broader usability, open data standards, inter-institutional protocols, and context-sensitive calibration are essential for replicating such digital infrastructure across diverse planning contexts.

Finally, with reference to Phase 3 of the study, conclusions are here addressed to the new research line opened by the study. According to the synthesis in Figure 4, the next step of the study is going to be directed at analyzing the specific usability paths, targeted to non-expert user contexts (local governance, academics and education, local stakeholders). To do this, a reinforcement of the LWI analysis has been programmed to select data and information specifically oriented to the above-mentioned user clusters. The expected output is a set of cognitive elements in order to complete the questionnaire proposed in Appendix B and to plan on-site surveys. This further phase of the study would be crucial to recognize how non-expert users develop knowledge capacities about both NbS planning and decision processes, within the overall functioning of the platform. This research should clearly address the conditions that enable digital decision-support systems to function as open, revisable, and context-sensitive knowledge infrastructure for climate-resilience. The ability to link digital innovation with governance and knowledge integration remains a central challenge for the evolution of decision-support tools in environmental planning.